International Journal of
Molecular Sciences
Article
Impact of Prematurity and Perinatal Antibiotics on
the Developing Intestinal Microbiota: A Functional
Inference Study
Silvia Arboleya 1 , Borja Sánchez 1 , Gonzalo Solís 2 , Nuria Fernández 3 , Marta Suárez 2 ,
Ana M. Hernández-Barranco 1 , Christian Milani 4 , Abelardo Margolles 1 ,
Clara G. de los Reyes-Gavilán 1 , Marco Ventura 4 and Miguel Gueimonde 1, *
1
2
3
4
*
Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de
Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain; [email protected] (S.A.);
[email protected] (B.S.); [email protected] (A.M.H.-B.); [email protected] (A.M.);
[email protected] (C.G.d.l.R.-G.)
Pediatrics Service, Hospital Universitario Central de Asturias, SESPA, 33006 Oviedo, Asturias, Spain;
[email protected] (G.S.); [email protected] (M.S.)
Pediatrics Service, Hospital de Cabueñes, SESPA, 33394 Gijón, Asturias, Spain; [email protected]
Laboratory of Probiogenomics, Department of Life Sciences, University of Parma, 43124 Parma, Italy;
[email protected] (C.M.); [email protected] (M.V.)
Correspondence: [email protected]; Tel.: +34-985-892-131; Fax: +34-985-892-233
Academic Editor: Patrick C. Y. Woo
Received: 17 February 2016; Accepted: 20 April 2016; Published: 29 April 2016
Abstract: Background: The microbial colonization of the neonatal gut provides a critical stimulus for
normal maturation and development. This process of early microbiota establishment, known to be
affected by several factors, constitutes an important determinant for later health. Methods: We studied
the establishment of the microbiota in preterm and full-term infants and the impact of perinatal
antibiotics upon this process in premature babies. To this end, 16S rRNA gene sequence-based
microbiota assessment was performed at phylum level and functional inference analyses were
conducted. Moreover, the levels of the main intestinal microbial metabolites, the short-chain fatty
acids (SCFA) acetate, propionate and butyrate, were measured by Gas-Chromatography Flame
ionization/Mass spectrometry detection. Results: Prematurity affects microbiota composition at
phylum level, leading to increases of Proteobacteria and reduction of other intestinal microorganisms.
Perinatal antibiotic use further affected the microbiota of the preterm infant. These changes involved
a concomitant alteration in the levels of intestinal SCFA. Moreover, functional inference analyses
allowed for identifying metabolic pathways potentially affected by prematurity and perinatal
antibiotics use. Conclusion: A deficiency or delay in the establishment of normal microbiota function
seems to be present in preterm infants. Perinatal antibiotic use, such as intrapartum prophylaxis,
affected the early life microbiota establishment in preterm newborns, which may have consequences
for later health.
Keywords: intestinal microbiota; microbiome; preterm; infants; antibiotics
1. Introduction
Increasing knowledge about the important role the intestinal microbiota play in human health
has attracted the attention of researchers to factors that determine its development in the host. During
the last decade the improvement and availability of next-generation DNA sequencing techniques has
allowed for carrying out detailed gut microbiota studies, increasing enormously our understanding of
its composition and activity [1–7]. The microbiota varies along the gastrointestinal tract, corresponding
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Int. J. Mol. Sci. 2016, 17, 649
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with different ecological niches from the mouth to the colon. The upper bowel is sparsely populated,
while the colon is heavily colonized, with levels that may reach 1011 –1012 bacteria per gram of
content [8].
The key role of early life events in determining the microbiota establishment process in the
newborn, and their impact on later health, is currently well recognized [9,10]. This early colonization
is essential for adequate infant development, constituting the basis for the later physiological,
immunological, and neurological homeostasis of the individual [11–14].
The process of establishment of the microbiota starts at birth and is driven by the interplay
between genetic factors, mode of delivery, environment, and feeding mode and later diet [15]. Recent
studies have monitored the establishment and development of the microbiota in the first days of infant
life [16,17], increasing our understanding of the step-wise evolution of this process. Fecal metabolome
studies have also been carried out underlining the strong relationship between the development
of the microbiota and that of the metabolic pathways in the infant gut [17]. During the very early
days the infant fecal microbiota seems to rely mainly in the catabolism of proteins with production
of free amino acids and branched chain fatty acids, with high levels of proteobacteria. Then the
infant microbiota turns towards the metabolism of carbohydrates with a concomitant increase in the
production of short-chain fatty acids (SCFA) [17]. This initial intestinal colonization process, in spite of
being a critical moment for microbiota development and modulation, remains poorly understood with
significant gaps in the knowledge about the factors affecting it, perhaps with the exception of the mode
of delivery and feeding patterns [16,18,19]. In spite of the existing gaps, a recent study has started
to take advantage of this knowledge and demonstrated the beneficial effect, in terms of microbiota
modulation, of vaginal microbiota transfer in those cases in which the microbiota establishment process
may be challenged, as is the case in C-section delivered babies [20].
Prematurity is known to affect different systems; these infants are born with an immature immune
system [21], which limits their resistance to infection, thereby increasing their risk of disease. In a
previous work [22] we studied the process of establishment of the intestinal microbiota in very-low
birth-weight (VLBW) preterm infants and compared it with that of healthy full-term, vaginally
delivered, exclusively breast-fed (FTVDBF) neonates by 16S rRNA gene profiling and quantitative
PCR for different microbial groups. The results obtained showed clear differences in the process of
intestinal microbiota development in VLBW preterm infants, which is in good agreement with previous
observations [23–28]. In addition, we found that perinatal antibiotic use, including intrapartum
antimicrobial prophylaxis (IAP), affects the gut microbiota development during the critical first
weeks of life. These results have been further confirmed by more recent studies [29] underlining the
importance of IAP as a factor influencing the microbiota establishment in the newborn.
In the present work, we analyzed the data from our previous study [22] at phylum levels and
further extend our study by conducting a functional inference analysis to determine the potential
impact of prematurity and perinatal antibiotics upon the genes present in the intestinal microbiota.
Moreover, we determined the levels of the main intestinal bacterial metabolites, the fecal SCFA acetate,
propionate and butyrate, and compared them among the infant groups.
2. Results and Discussion
2.1. Establishment of the Intestinal Microbiota in Preterm and Full-Term Infants
The analyses of the bacterial phyla present in fecal samples from VLBW preterm and FTVDBF
babies evidenced clear differences between both groups of infants during the first months of life
(Figure 1). Preterm neonates harbored a significantly higher (p < 0.01) relative proportion of Firmicutes
at two days of age, and of Proteobacteria in the later sampling times (p < 0.01 at 10 and p < 0.001 at
30 days of age), than FTVDBF babies. By contrast, premature infants showed reduced levels (p < 0.05)
of Bacteroidetes at day 2 of life and of this phylum and that of Actinobacteria during the first month,
the differences remaining significant (p < 0.05) for up to three months in the case of the phylum
Int. J. Mol. Sci. 2016, 17, 649
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Bacteroidetes.
In17,
FTVDBF
babies Proteobacteria, followed by Firmicutes and Bacteroidetes, dominated
the microbiota at two days of age, while in preterms Firmicutes followed by Actinobacteria and
by Actinobacteria
and
Proteobacteria
the (Figure
predominant
10 daysnewborns
of age in
Proteobacteria
were
the
predominantwere
groups
1). Atgroups
10 days(Figure
of age1).
in At
FTVDBF
FTVDBF
newborns
Proteobacteria
and
Firmicutes
co-dominate
with
a
slight
increase
the
Proteobacteria and Firmicutes co-dominate with a slight increase in the percentage of sequencesinfrom
percentage of and
sequences
from Bacteroidetes
situation
that remained
Bacteroidetes
Actinobacteria,
a situation and
that Actinobacteria,
remained stableawith
only minor
changes stable
duringwith
the
only
minor
changes
during
the
rest
of
the
study.
However,
at
the
same
age
(10
days)
Proteobacteria
rest of the study. However, at the same age (10 days) Proteobacteria had become the clearly dominant
had becomeinthe
clearly
dominant
population
VLBW
pretermActinobacteria,
infants, followed
Firmicutes,
population
VLBW
preterm
infants,
followedinby
Firmicutes,
andby
Bacteroidetes.
Actinobacteria,
and
Bacteroidetes.
This
situation
remained
unchanged
during
the
first
month
of and
life,
This situation remained unchanged during the first month of life, with the levels of Firmicutes
with
the
levels
of
Firmicutes
and
Actinobacteria
increasing
later
on
(at
90
days)
(Figure
1).
Actinobacteria increasing later on (at 90 days) (Figure 1).
FTVDBF
PRETERM
2 days
Actinobacteria
10 days
Bacteroidetes
30 days
Firmicutes
Proteobacteria
90 days
Others
Figure 1.1.Aggregate
Aggregatemicrobiota
microbiota
at phylum
level
fecal samples
from full-term,
vaginally
Figure
(%)(%)
at phylum
level in
fecalinsamples
from full-term,
vaginally delivered,
delivered, exclusively
breast-fed vs.
(FTVDBF)
preterm
infants
at the
different
points analyzed.
exclusively
breast-fed (FTVDBF)
pretermvs.
infants
at the
different
time
pointstime
analyzed.
Previous studies reported the impact of prematurity upon the process of development of the
Previous studies reported the impact of prematurity upon the process of development of the
intestinal microbiota in the neonate [25,27,30–33]. Our results on the bacterial phyla present in the
intestinal microbiota in the neonate [25,27,30–33]. Our results on the bacterial phyla present in
samples, which confirm our previous data, show noticeable differences in the gut microbiota
the samples, which confirm our previous data, show noticeable differences in the gut microbiota
composition between preterm and FTVDBF babies [22,31].
composition between preterm and FTVDBF babies [22,31].
Despite the high inter-individual variation, and in accordance with the observed alterations on
Despite the high inter-individual variation, and in accordance with the observed alterations
the fecal microbiota composition, differences in the levels of SCFA between VLBW preterm and
on the fecal microbiota composition, differences in the levels of SCFA between VLBW preterm and
FTVDBF infants were also observed. As expected, acetate was the major SCFA in both groups of
FTVDBF infants were also observed. As expected, acetate was the major SCFA in both groups of
infants, followed by propionate and low levels of butyrate. We found a significantly (p < 0.05) lower
infants, followed by propionate and low levels of butyrate. We found a significantly (p < 0.05) lower
concentration of total fecal SCFA in our low-birthweight preterm infants when compared with the
concentration of total fecal SCFA in our low-birthweight preterm infants when compared with the
FTVDBF ones. These differences were evident at the first sampling points but tended to disappear
FTVDBF ones. These differences were evident at the first sampling points but tended to disappear
along the study period (Figure 2). These results are in good agreement with previously reported data
along the study period (Figure 2). These results are in good agreement with previously reported
obtained in non-low-birthweight premature babies [25]. The alteration in the SCFA pattern in
data obtained in non-low-birthweight premature babies [25]. The alteration in the SCFA pattern
premature babies indicates a strong metabolic effect of the observed differences in microbiota
in premature babies indicates a strong metabolic effect of the observed differences in microbiota
composition, suggesting an important alteration of the intestinal microbiota’s functionalities.
composition, suggesting an important alteration of the intestinal microbiota’s functionalities.
Int. J. Mol. Sci. 2016, 17, 649
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Figure
Concentration(mean
(mean± ˘
SD)
main
SCFAs
(acetate,
propionate,
butyrate)
and
Figure 2.
2. Concentration
SD)
of of
thethe
main
SCFAs
(acetate,
propionate,
and and
butyrate)
and total
total
SCFAs
in fecal
samples
full-term,
vaginally
delivered,
exclusively
breast-fed
(FTVDBF)
SCFAs
in fecal
samples
fromfrom
full-term,
vaginally
delivered,
exclusively
breast-fed
(FTVDBF)
vs.
vs.
preterm
infants
at
the
different
time
points
analyzed
(2,
10,
30,
and
90
days).
Asterisks
indicate
preterm infants at the different time points analyzed (2, 10, 30, and
days). Asterisks indicate
statistically
statistically significant
significantdifferences
differences(*(*pp<< 0.05;
0.05; **
** pp << 0.01,
0.01, ***
*** pp << 0.001).
In this
this regard,
regard, when
when the
the results
results of
of the
the functional
functional inference
inference analyses
analyses of
of both
both groups
groups of
of infants
infants
In
(VLBW
preterm
and
FTVDBF)
were
compared,
we
found
differences
between
them.
By
collapsing
(VLBW preterm and FTVDBF) were compared, we found differences between them. By collapsing
the data
data at
at different
different KEGG
KEGG levels
levels we
we found
found the
the KEGG
KEGG level
level 11 categories
categories Metabolism,
Metabolism, Cellular
Cellular
the
Processes,
Environmental
Information
Processing,
and
Genetic
Information
Processing
to
be
the
Processes, Environmental Information Processing, and Genetic Information Processing to bemost
the
affected,
with with
mostmost
of the
pathways
at at
KEGG
statistically significant
significant
most
affected,
of the
pathways
KEGGlevel
level2 2also
also displaying
displaying statistically
differences (p
(p << 0.05
preterm
and
FTVDBF
infants
(Table
1). Metabolism
was
differences
0.05 and
andqq<<0.25)
0.25)between
between
preterm
and
FTVDBF
infants
(Table
1). Metabolism
the
most
affected
category,
with
preterm
infants
showing
a
significantly
(p
<
0.05
and
q
<
0.25)
higher
was the most affected category, with preterm infants showing a significantly (p < 0.05 and q < 0.25)
frequency
of genesoffrom
thefrom
pathway
“xenobiotics
biodegradation
and metabolism”
along the whole
higher
frequency
genes
the pathway
“xenobiotics
biodegradation
and metabolism”
along
study
period,
but
without
reaching
statistically
significant
differences
at
90
days
of
life.
Similarly,
the whole study period, but without reaching statistically significant differences at 90 days of life.
preterm babies
significantly
higherhigher
frequencies
of genes
from
“lipid
Similarly,
pretermdisplayed
babies displayed
significantly
frequencies
of genes
fromthe
thepathways
pathways “lipid
metabolism” at
at two
two days
days of
of life
life and
and aa trend
trend toward
toward higher
higher levels
levels at
at 10
10 days,
days, “metabolism
“metabolism of
of other
other
metabolism”
amino
acids”,
“metabolism
of
terpenoids
and
polyketides”,
and
“nucleotide
metabolism”
at
two
days
amino acids”, “metabolism of terpenoids and polyketides”, and “nucleotide metabolism” at two days
of age
age(Table
(Table1).1).On
On
the
other
hand,
preterm
babies
showed
lower
numbers
of genes
belonging
to
of
the
other
hand,
preterm
babies
showed
lower
numbers
of genes
belonging
to other
other
pathways
such
as
“energy
metabolism”,
“enzyme
families”,
“glycan
biosynthesis
and
pathways such as “energy metabolism”, “enzyme families”, “glycan biosynthesis and metabolism”,
metabolism”,of“metabolism
cofactors and
vitamins”,of“biosynthesis
of other
secondary“carbohydrate
metabolites”,
“metabolism
cofactors andofvitamins”,
“biosynthesis
other secondary
metabolites”,
“carbohydrateormetabolism”,
or “amino acid metabolism”.
metabolism”,
“amino acid metabolism”.
Regarding
the
categories
Cellular Processes,
Processes, Genetic
Genetic Information
Information Processing,
Processing, and
and Environmental
Environmental
Regarding the categories Cellular
Information
Processing,
preterm
infants
showed
a
higher
relative
frequency
(p
<
0.05
q < than
0.25)
Information Processing, preterm infants showed a higher relative frequency (p < 0.05 and qand
< 0.25)
than FTVDBF
of inferred
genes belonging
to the pathways
“cell
growth
and
FTVDBF
infantsinfants
of inferred
genes belonging
to the pathways
“cell growth
and
death”and
anddeath”
“signaling
“signaling
molecules
and
interaction”
at
two
days
of
age,
while
the
opposite
was
observed
for
the
molecules and interaction” at two days of age, while the opposite was observed for the later sampling
later sampling
(Table
1). A similar
behavior
observed
inferredtogenes
belonging toand
the
points
(Table 1).points
A similar
behavior
was observed
forwas
inferred
genesfor
belonging
the “translation”
“translation”
and
“replication
and
repair”
pathways.
The
opposite
behavior—lower
levels
at
two
“replication and repair” pathways. The opposite behavior—lower levels at two days in preterm infants
days
in preterm
infants but
higherfor
later
observed
formotility”,
the “transcription”,
motility”,
but
higher
later on—was
observed
theon—was
“transcription”,
“cell
and “signal“cell
transduction”
and “signalMoreover,
transduction”
pathways.
Moreover,
in general,
lower
frequencies
inferred genes
from
pathways.
in general,
lower
frequencies
of inferred
genes
from theofpathways
“transport
the
pathways
“transport
and
catabolism”
and
“folding,
sorting,
and
degradation”
were
observed
in
and catabolism” and “folding, sorting, and degradation” were observed in preterm than in full-term
preterm
than
in
full-term
infants
during
the
study
period,
the
contrary
being
true
for
“membrane
infants during the study period, the contrary being true for “membrane transport” (Table 1).
transport” (Table 1).
Int. J. Mol. Sci. 2016, 17, 649
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Table 1. Relative frequencies (mean ˘ standard deviation (SD)) at the different time points of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (level 2)
belonging to the categories that showed statistically significant differences between preterm and full-term infants. Asterisk denotes statistically significant differences
(p < 0.05 and q < 0.25) after Fañse Discovery Rate (FDR) correction; ns: Non-statistically significant differences.
Day 2
KEGG Pathways
Infants
Mean ˘ SD
p
(t-Test)
Day 10
q-Value
Mean ˘ SD
p
(t-Test)
Day 30
q-Value
Mean ˘ SD
p
(t-Test)
Day 90
q-Value
Mean ˘ SD
p
(t-Test)
q-Value
Metabolism
Amino Acid Metabolism
Preterm
Term
8.68 ˘ 0.58
8.43 ˘ 1.02
ns
0.558
8.29 ˘ 0.64
9.06 ˘ 0.78
*
0.031
8.35 ˘ 0.48
8.86 ˘ 0.61
*
0.053
8.61 ˘ 0.66
9.00 ˘ 0.67
ns
0.263
Biosynthesis of Other
Secondary Metabolites
Preterm
Term
0.77 ˘ 0.06
0.74 ˘ 0.24
ns
0.776
0.66 ˘ 0.08
0.86 ˘ 0.24
*
0.037
0.70 ˘ 0.10
0.85 ˘ 0.19
*
0.056
0.75 ˘ 0.11
0.82 ˘ 0.22
ns
0.391
Carbohydrate Metabolism
Preterm
Term
11.53 ˘ 0.64
11.16 ˘ 1.08
ns
0.383
10.99 ˘ 0.77
11.04 ˘ 0.80
ns
0.902
10.86 ˘ 0.7
11.31 ˘ 0.73
*
0.131
10.88 ˘ 0.60
11.11 ˘ 0.76
ns
0.497
Energy Metabolism
Preterm
Term
4.93 ˘ 0.19
5.08 ˘ 0.63
ns
0.544
4.72 ˘ 0.12
5.2 ˘ 0.52
*
0.033
4.75 ˘ 0.13
5.13 ˘ 0.42
*
0.029
4.91 ˘ 0.31
5.26 ˘ 0.46
*
0.550
Enzyme Families
Preterm
Term
2.09 ˘ 0.07
2.08 ˘ 0.15
ns
0.809
2.03 ˘ 0.05
2.13 ˘ 0.10
*
0.039
2.03 ˘ 0.04
2.13 ˘ 0.09
*
0.023
2.09 ˘ 0.07
2.13 ˘ 0.09
ns
0.327
Glycan Biosynthesis
and Metabolism
Preterm
Term
1.89 ˘ 0.28
2.73 ˘ 0.82
*
0.027
2.25 ˘ 0.38
2.71 ˘ 0.78
*
0.112
2.35 ˘ 0.25
2.60 ˘ 0.67
ns
0.307
2.22 ˘ 0.31
2.50 ˘ 0.73
ns
0.343
Lipid Metabolism
Preterm
Term
3.08 ˘ 0.24
2.83 ˘ 0.16
*
0.006
2.89 ˘ 0.30
2.81 ˘ 0.22
ns
0.493
2.78 ˘ 0.12
2.82 ˘ 0.19
ns
0.561
2.85 ˘ 0.17
2.90 ˘ 0.22
ns
0.640
Metabolism of Cofactors
and Vitamins
Preterm
Term
3.55 ˘ 0.55
4.07 ˘ 0.52
*
0.044
3.80 ˘ 0.19
4.14 ˘ 0.39
*
0.032
3.88 ˘ 0.26
4.02 ˘ 0.34
ns
0.291
3.89 ˘ 0.13
4.03 ˘ 0.18
ns
0.387
Metabolism of Other
Amino Acids
Preterm
Term
1.66 ˘ 0.06
1.63 ˘ 0.07
*
0.230
1.68 ˘ 0.08
1.69 ˘ 0.13
ns
0.892
1.70 ˘ 0.06
1.68 ˘ 0.07
ns
0.367
1.65 ˘ 0.13
1.61 ˘ 0.14
ns
0.510
Metabolism of Terpenoids
and Polyketides
Preterm
Term
1.74 ˘ 0.13
1.57 ˘ 0.20
*
0.052
1.50 ˘ 0.19
1.53 ˘ 0.14
ns
0.620
1.44 ˘ 0.08
1.53 ˘ 0.09
*
0.034
1.46 ˘ 0.08
1.52 ˘ 0.12
ns
0.295
Nucleotide Metabolism
Preterm
Term
4.27 ˘ 0.52
3.85 ˘ 0.63
*
0.125
3.23 ˘ 0.63
3.78 ˘ 0.57
*
0.030
3.27 ˘ 0.57
3.70 ˘ 0.51
*
0.064
3.40 ˘ 0.46
3.67 ˘ 0.40
ns
0.279
Xenobiotics Biodegradation
and Metabolism
Preterm
Term
2.57 ˘ 0.35
1.80 ˘ 0.15
*
0.000
2.23 ˘ 0.35
1.84 ˘ 0.35
*
0.082
2.18 ˘ 0.28
1.87 ˘ 0.31
*
0.036
2.08 ˘ 0.41
1.80 ˘ 0.39
ns
0.269
Int. J. Mol. Sci. 2016, 17, 649
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Table 1. Cont.
Day 2
KEGG Pathways
Infants
Cell Growth and Death
Preterm
Term
0.50 ˘ 0.09
0.40 ˘ 0.11
Cell Motility
Preterm
Term
Transport and Catabolism
p
(t-Test)
Day 10
p
(t-Test)
Day 90
q-Value
Mean ˘ SD
p
(t-Test)
*
0.034
0.30 ˘ 0.08
0.40 ˘ 0.08
*
0.027
0.35 ˘ 0.10
0.41 ˘ 0.08
ns
0.289
2.06 ˘ 1.10
1.67 ˘ 1.30
ns
0.496
1.98 ˘ 0.97
1.51 ˘ 0.59
*
0.121
1.84 ˘ 0.81
1.78 ˘ 0.91
ns
0.930
0.18 ˘ 0.03
0.32 ˘ 0.19
*
0.049
0.19 ˘ 0.03
0.29 ˘ 0.17
*
0.085
0.20 ˘ 0.06
0.31 ˘ 0.18
ns
0.317
0.120
2.19 ˘ 0.10
2.31 ˘ 0.20
*
0.112
2.20 ˘ 0.11
2.32 ˘ 0.14
ns
0.749
*
0.046
6.50 ˘ 0.98
7.60 ˘ 1.07
*
0.035
7.00 ˘ 1.14
7.76 ˘ 0.99
ns
0.360
3.02 ˘ 0.18
2.73 ˘ 0.31
*
0.029
3.00 ˘ 0.19
2.86 ˘ 0.27
*
0.191
3.05 ˘ 0.20
2.90 ˘ 0.26
ns
0.261
3.93 ˘ 0.99
4.81 ˘ 0.99
*
0.039
3.93 ˘ 0.82
4.68 ˘ 0.80
*
0.045
4.19 ˘ 0.73
4.57 ˘ 0.58
ns
0.266
0.048
16.93 ˘ 1.31
14.75 ˘ 3.11
*
0.075
16.54 ˘ 1.59
14.53 ˘ 2.68
ns
0.292
*
0.066
2.44 ˘ 0.47
1.96 ˘ 0.31
*
0.009
2.23 ˘ 0.47
1.93 ˘ 0.49
ns
0.283
*
0.071
0.15 ˘ 0.06
0.21 ˘ 0.06
*
0.039
0.17 ˘ 0.05
0.20 ˘ 0.07
ns
0.353
q-Value
Mean ˘ SD
*
0.064
0.30 ˘ 0.09
0.40 ˘ 0.10
1.12 ˘ 0.47
1.85 ˘ 1.31
*
0.151
Preterm
Term
0.20 ˘ 0.03
0.29 ˘ 0.18
*
0.200
Folding, Sorting
and Degradation
Preterm
Term
2.20 ˘ 0.05
2.40 ˘ 0.28
*
0.100
2.18 ˘ 0.14
2.34 ˘ 0.28
*
Replication and Repair
Preterm
Term
8.76 ˘ 1.06
7.91 ˘ 1.29
*
0.126
6.52 ˘ 1.21
7.88 ˘ 1.30
Transcription
Preterm
Term
2.70 ˘ 0.19
2.72 ˘ 0.29
ns
0.839
Translation
Preterm
Term
5.70 ˘ 0.82
4.74 ˘ 1.08
*
0.053
Membrane Transport
Preterm
Term
15.15 ˘ 1.01
13.91 ˘ 3.38
ns
0.345
16.86 ˘ 1.57
14.05 ˘ 3.71
*
Signal Transduction
Preterm
Term
1.56 ˘ 0.43
2.05 ˘ 0.71
*
0.105
2.50 ˘ 0.49
1.89 ˘ 0.58
Signaling Molecules
and Interaction
Preterm
Term
0.27 ˘ 0.07
0.22 ˘ 0.08
*
0.147
0.16 ˘ 0.08
0.22 ˘ 0.08
Mean ˘ SD
Day 30
q-Value
Mean ˘ SD
p
(t-Test)
q-Value
Cellular Processes
Genetic Information Processing
Environmental Information Processing
Int. J. Mol. Sci. 2016, 17, 649
7 of 14
The functional inference analysis carried out suggests an increasing ability of the preterm
infant microbiota to metabolize xenobiotics and a trend toward increased presence of genes related
with cell motility, likely related to the dominance of Proteobacteria in this infant group. Moreover,
a concomitant reduction in the presence of common intestinal microbiota metabolic activities such
as glycan metabolism, or metabolism of vitamins, was observed in VLBW preterm infants when
compared with FTVDBF babies, suggesting a reduced presence of genes related to the functions of the
normal microbiota. In general, as was observed for the microbial composition data, the differences
tended to be reduced over time, in most cases having disappeared by the age of 90 days (Table 1).
A good correlation between total metagenomics and functional inference from 16S rDNA gene profiling
has been previously reported [34], underlining the interest of performing functional inference analyses
when such data are available. It is important to underline that at the end of the study none of the
preterm infants had been exclusively breast-fed or formula-fed and the exposure to breastmilk was
highly variable, as is often the case among preterm babies, while our control group included only
exclusively breast-fed babies. Therefore, the potential impact of the different feeding habits cannot be
overruled as a factor explaining the differences observed between both groups of infants.
In accordance with previous studies [22,25,31], our results suggest a deficiency or delay in the
establishment of a normal microbiota function in the preterm infant gut. Although this deficiency
seems to have been mostly overcome by the age of three months, given the important role of the early
days of life for the maturation of the immune system [21], it could be hypothesized that this alteration
in the early colonization may pose a risk for later health.
2.2. Effect of Perinatal Antibiotics on Microbiota Development in Preterm Infants
In this study we compared four groups of VLBW preterm infants established on the basis of
the administration of antibiotics to the mother (Intrapartum antimicrobial prophylaxis, IAP), to the
infant itself, to both mother and infant, or to none of them (no antibiotic-exposed infants). The results
obtained confirmed, at a high taxonomic level, the previously reported effect of perinatal antibiotics
(including IAP) upon the preterm newborn microbiota [22]. Now the comparison of the 16S rRNA
profiling data at phylum level, among the different antibiotic exposure groups, allowed us to identify
antibiotic-related effects upon the microbiota composition. Interestingly, these effects were not so
apparent in the first days of life, when no statistically significant differences on the bacterial phyla were
observed among the four preterm infant groups (data not shown), as after 30 days when statistically
significant differences were found for Actinobacteria (p < 0.05), Firmicutes (p < 0.01), and Proteobacteria
(p < 0.01) (Figure 3). The relative frequency of the first phylum was significantly higher in antibiotic-free
preterm infants than in the groups where either the mother or the mother and the infant received
antibiotics (p < 0.01 and p < 0.05, respectively). The phylum Firmicutes showed significantly higher
levels in antibiotic-free babies with respect to the two infant groups where mothers received antibiotics
(p < 0.01 in both cases). Interestingly, this phylum was also found to be higher (p < 0.05) in the
antibiotic-receiving preterm babies whose mothers did not receive IAP than in the antibiotic-free
preterm babies whose mothers were administered antibiotics. The opposite behavior to that found for
Firmicutes was observed for the phylum Proteobacteria, whose levels were lower in antibiotic-free
newborns than in those cases in which either the mother (p < 0.05) or the mother and the infant
(p < 0.01) received antibiotics. Moreover, higher Proteobacteria levels (p < 0.05) were found in infants
whose mothers received IAP than in the group in which the infants but not the mothers received the
antibiotics (Figure 3).
Int. J.J. Mol.
Mol. Sci. 2016, 17, 649
Int.
649
of 14
14
88 of
Figure
Figure 3.
3. Relative abundance
abundance (%) of phylum level distributions
distributions of
of the
the fecal microbiota
microbiota in
in the different
different
antibiotic
antibiotic exposure
exposure groups
groups classified
classified into
into four
four classes
classes depending
depending on
on mother
mother and
and infant
infant antibiotic
antibiotic
exposure
exposure at
at 30
30 days
days of
of life.
life.
Our results
results support
support and
and extend
extend previous
previous data
data indicating
indicating an
an effect
effect of
of perinatal
perinatal exposure
exposure to
to
Our
antibiotics on
on the
the establishing
establishing microbiota.
microbiota. Increases
Increases in
in family
family Enterobacteriaceae
Enterobacteriaceae (microorganisms
(microorganisms
antibiotics
belonging
to
the
phylum
Proteobacteria)
following
antibiotic
exposure
in
neonates
have been
been
belonging to the phylum Proteobacteria) following antibiotic exposure in neonates have
repeatedly
observed
[22,29,35–37].
Although
some
authors
did
not
find
any
effect
of
maternal
repeatedly observed [22,29,35–37]. Although some authors did not find any effect of maternal
antibioticsuse
useduring
during
pregnancy
microbiota
development
in full-term
[38],studies
other
antibiotics
pregnancy
on on
the the
microbiota
development
in full-term
infantsinfants
[38], other
studies
did
[36].
Moreover,
in
the
specific
case
of
IAP,
microbiota
alterations
such
as
a
reduction
did [36]. Moreover, in the specific case of IAP, microbiota alterations such as a reduction in the levels of
in the
levels
of the family
Bacteroidaceae
observed
in both
preterm
and full-term
the
family
Bacteroidaceae
have been
observed inhave
bothbeen
preterm
and full-term
infants
[22,29,39].
Notably,
infants
[22,29,39].
Notably,
the
observed
effect
of
antibiotics
was
more
pronounced
when
the observed effect of antibiotics was more pronounced when the antibiotics were administeredthe
to
antibiotics
administered
to thethat
mother
delivery
(IAP)
that when
infant itself
received
the
motherwere
during
delivery (IAP)
whenduring
the infant
itself
received
them.the
Moreover,
it was
also
them. Moreover,
was this
alsoalteration
interestingontothe
note
that this alteration
on the
microbiota
development
interesting
to noteit that
microbiota
development
process
was not
so evident
process
was
not
so
evident
during
the
first
days
of
life
as
at
the
end
of
the
first
month,
having
during the first days of life as at the end of the first month, having almost disappeared by the almost
age of
disappeared
three
months.by the age of three months.
In accordance
accordance with
with the
the microbial
microbial data,
data, the
the concentration
concentration of
of SCFA
SCFA was
was also
also found
found to
to be
be affected,
In
affected,
especially
by
the
IAP
administration
to
the
mothers.
The
high
inter-individual
variability
on SCFA
SCFA
especially by the IAP administration to the mothers. The high inter-individual variability on
levels isislikely
likelyto to
have
prevented
the detection
any statistically
significant
difference
among
levels
have
prevented
the detection
of anyofstatistically
significant
difference
among the
four
the
four
groups
of
infants.
Nevertheless,
at
30
days
of
age
infants
not
exposed
to
antibiotics
at
all,
groups of infants. Nevertheless, at 30 days of age infants not exposed to antibiotics at all, or those who
or
those
who
received
them
but
whose
mothers
did
not,
showed
a
trend
towards
higher
levels
of
received them but whose mothers did not, showed a trend towards higher levels of acetic (p = 0.075)
acetic
(p =(p0.075)
andSCFA
total (p
= 0.060)
SCFA than
the newborns
mothers
received
and
total
= 0.060)
than
the newborns
whose
mothers whose
received
IAP (Table
S1). IAP (Table S1).
The
results
of
the
functional
inference
analyses
showed
differences
among
the four
four different
different
The results of the functional inference analyses showed differences among the
antibiotic
exposure
groups.
In
accordance
to
the
microbiota
composition
data,
the
age
of
30
days
was
antibiotic exposure groups. In accordance to the microbiota composition data, the age of 30 days was
the time
time at
at which
which statistically
statistically significant
significant differences
differences (p
(p << 0.05
0.05 and
and qq << 0.25)
0.25) among
among the
the groups
groups were
were
the
observed
for
more
functional
pathways
(Figure
4).
The
categories
Cellular
Processes,
Metabolism,
observed for more functional pathways (Figure 4). The categories Cellular Processes, Metabolism,
Environmental Information
Information Processing,
Processing, and
and Genetic
Genetic Information
Information Processing,
Environmental
Processing, were
were the
the most
most affected.
affected.
Interestingly,
the
administration
of
IAP
to
the
mothers
was
the
most
influential
factor.
The
relative
Interestingly, the administration of IAP to the mothers was the most influential factor. The relative
frequency
of
inferred
genes
belonging
to
the
pathways
“cell
growth
and
death,”
“biosynthesis
of
frequency of inferred genes belonging to the pathways “cell growth and death,” “biosynthesis of other
other secondary
metabolites,”
“nucleotide
metabolism,”
molecules
and interaction,”
secondary
metabolites,”
“nucleotide
metabolism,”
“signaling“signaling
molecules and
interaction,”
“replication
“replication
and
repair,”
and
“translation”
were
significantly
higher
(p
<
0.05
and
< 0.25) in the
and repair,” and “translation” were significantly higher (p < 0.05 and q < 0.25) in theqantibiotic-free
antibiotic-free
babies
grouptowith
respect
to the
groups
of babies
whose
mothers
IAP.
babies
group with
respect
the two
groups
oftwo
babies
whose
mothers
received
IAP. received
Interestingly,
Interestingly,
differences
were
for the abovementioned
pathways
between
the groups of
no
differences no
were
evident for
theevident
abovementioned
pathways between
the groups
of antibiotics-free
antibiotics-free
babies
and
the
antibiotic-receiving
preterm
newborns
whose
mothers
did
notopposite
receive
babies and the antibiotic-receiving preterm newborns whose mothers did not receive IAP. The
IAP. The opposite behavior was observed for the relative frequency of inferred genes belonging to
“signal transduction” (Figure 4), for which higher levels were observed in the infant groups whose
Int. J. Mol. Sci. 2016, 17, 649
9 of 14
behavior
was
observed
Int.
J. Mol. Sci.
2016,
17, 649
for the relative frequency of inferred genes belonging to “signal transduction”
9 of 14
(Figure 4), for which higher levels were observed in the infant groups whose mothers received IAP
mothers
received
than intobabies
not exposed
to antibiotics.
The functional
inference
than in babies
notIAP
exposed
antibiotics.
The functional
inference
analyses carried
outanalyses
suggest
carried
outpotentially
suggest pathways
potentially
byantibiotics,
the use of perinatal
mainly oftoIAP
pathways
affected by
the use ofaffected
perinatal
mainly ofantibiotics,
IAP administration
the
administration
to the pregnant woman.
pregnant woman.
0.5
Cellular Processes
Metabolism
5.00
a
a,b
a
a,b
b
b
b
a
0.25
a,b
b
2.50
a,b
b
Relative frequency (%)
a
0.00
a,b
b
b
0.00
Cell Growth and Death
Transport and Catabolism
Environmental Information
Processing
3.5
a,b
b
Biosynthesis of Other Secondary
Metabolites
9.00
b
Nucleotide Metabolism
Genetic Information Processing
a
a,b
b
b
a
a
1.75
a,b
4.50
b
a
b
a,b
b
b
0.00
0.00
Signal Transduction
Signaling Molecules and Interaction
Replication and Repair
Translation
Mother No Antibiotics – Infant No Antibiotics
Mother No Antibiotics – Infant Antibiotics
Mother Antibiotics – Infant No Antibiotics
Mother Antibiotics – Infant Antibiotics
Figure
Figure 4.
4. Relative
Relative frequencies
frequencies (mean
(mean ±˘SD)
SD)of
ofKEGG
KEGG pathways
pathways (level
(level 2)
2) belonging
belonging to
to the
the categories
categories
that
statistically significant
significant differences
differencesamong
amongthe
thefour
fourdifferent
differentantibiotic
antibioticexposure
exposure
groups
that showed
showed statistically
groups
at
at
30
days
of
life.
Different
letters
above
columns
within
the
same
KEGG
category
indicate
statistically
30 days of life. Different letters above columns within the same KEGG category indicate statistically
significant
significant differences
differences (p
(p << 0.05
0.05 and
and qq <<0.25).
0.25).
These results indicate an effect of perinatal antibiotics, mainly of IAP, on early life microbiota,
These results indicate an effect of perinatal antibiotics, mainly of IAP, on early life microbiota,
which may involve a lasting effect on the individual physiology [13]. The use of antibiotics may
which may involve a lasting effect on the individual physiology [13]. The use of antibiotics
influence microbiota-host crosstalk during the neonatal period, which may have profound
may influence microbiota-host crosstalk during the neonatal period, which may have profound
consequences for later health [15]. Actually, perinatal antibiotics exposure has been reported to
consequences for later health [15]. Actually, perinatal antibiotics exposure has been reported to
increase the risk of later disease such as asthma [40,41]. Given that the estimated use of IAP is over
increase the risk of later disease such as asthma [40,41]. Given that the estimated use of IAP is over 30%
30% of total deliveries [42], greater attention should be paid to its potential impact upon the gut
of total deliveries [42], greater attention should be paid to its potential impact upon the gut microbiota.
microbiota. This impact should be considered as a factor in the decision on whether or not to
This impact should be considered as a factor in the decision on whether or not to administer IAP.
administer IAP. In general, intrapartum prophylaxis is recommended in the case of premature
In general, intrapartum prophylaxis is recommended in the case of premature rupture of membranes
rupture of membranes and, in some countries in which a screening protocol is established, when
and, in some countries in which a screening protocol is established, when vaginal group B streptococci
vaginal group B streptococci colonization is observed [43,44]. However, there are other cases in which
colonization is observed [43,44]. However, there are other cases in which antibiotics are administered
antibiotics are administered without a clear benefit [45,46] and in which the use of IAP would be
without a clear benefit [45,46] and in which the use of IAP would be arguable, especially since we are
arguable, especially since we are starting to understand their impact on the early microbiota
starting to understand their impact on the early microbiota development and the importance of this
development and the importance of this process for later health [7,9,47]. It would be advisable to
process for later health [7,9,47]. It would be advisable to develop strategies, such as the concomitant
develop strategies, such as the concomitant administration of probiotics, aimed at limiting the impact
administration of probiotics, aimed at limiting the impact of IAP in the establishing microbiota in those
of IAP in the establishing microbiota in those cases in which IAP is required.
cases in which IAP is required.
Int. J. Mol. Sci. 2016, 17, 649
10 of 14
3. Materials and Methods
3.1. Volunteers and Samples
Thirteen Caucasian FTVDBF infants and 27 Caucasian very low birthweight (VLBW) preterm
infants from a previous work [22] were included in this study. Fourteen of the preterm infants’
mothers received intrapartum antimicrobial prophylaxis (IAP) (penicillin, ampicillin, or ampicillin
plus erythromycin). Twelve infants received antibiotics already during the first week of life, while five
additional infants started antibiotic treatment during the second week. Only five out of the
27 mother/premature infant pairs were not exposed to antibiotics, either intrapartum or postnatally,
and in nine of the pairs the mother received IAP and the infant postnatal antibiotics. Fecal samples
were collected at the hospital between 24 and 48 h of life and at 10, 30, and 90 days of age, immediately
frozen at ´20 ˝ C and processed as described by Arboleya and co-workers [22].
3.2. Intestinal Microbiota Analyses
The data from the abovementioned previous study [22], deposited at the National Center for
Biotechnology Information (ShortRead Archive BioProject ID PRJNA230470), were now used to assess
microbial composition at high phylogenetic (phylum) level. These data were obtained from sequencing
of amplicons covering the V3 region generated with optimized primers [48]. UCLUST software and
the Ribosomal Database Project were used for phylogenetic assignations.
3.3. Determination of SCFA in Feces
The analysis of short chain fatty acids (SCFA) was carried out in a chromatographic system
composed of two 6890N GC (Agilent Technologies Inc., Palo Alto, CA, USA) connected to a FID and a
MS 5973N detector as described previously [25].
3.4. Functional Inference Analysis
The functionality of the different metagenomes was predicted using the software PICRUSt 1.0.0
(http://picrust.github.com) [49]. In short, this software allows the prediction of functional KEGG
pathways abundances from the 16S rDNA reads. Firstly, a collection of closed reference OTUs was
obtained from the filtered reads using QIIME v1.7.0 [50] by querying the data against the IMG/GG
reference collection (version 13.5, May 2013, http://greengenes.secondgenome.com). Reverse strand
matching was enabled during the query and OTUs were picked at a 97% identity. A BIOM-formatted
table [51] was obtained with the pick_closed_reference_otus.py script. This table, containing the
relative abundances of the different reference OTUs in all the metagenomes, was normalized by the
predicted 16S rDNA copy number with the script normalize_by_copy_number.py. Final functional
predictions, inferred from the metagenomes, were created with the script predict_metagenomes.py.
When necessary, tab-delimited tables were obtained with the script convert_biom.py.
Predicted metagenomic contents were collapsed at two hierarchical KEGG pathway levels (levels 1
and 2) (http://www.genome.jp/kegg/pathway.html) with the categorize_by_function.py script. Each
of these tables was analyzed statistically in STAMP v2.0.0 [52]. Data of the KEGG pathway distributions,
at different hierarchical levels, were plotted with the script summarize_taxa_through_plots.py.
3.5. Statistical Analyses
In the functional inference analyses, the association of KEGG pathways at the different hierarchical
levels with the different grouping variables was identified by the two-sided Welch’s test for the analysis
of preterm and full-term grouping variables; Kruskal–Wallis was used for the analysis of the four
antibiotics grouping variables; and White´s non parametric t-test was used on every pairwise antibiotic
group comparison. The correction FDR [53] was finally applied in all cases and significant differences
in KEGG pathways between infant groups were only considered below a p-value of 0.05 and a q-value
Int. J. Mol. Sci. 2016, 17, 649
11 of 14
below 0.25 [34]. With regard to the microbiota composition and SCFA results were analyzed using
SPSS software (SPSS Inc., Chicago, IL, USA). The differences among infant groups were analyzed using
the non-parametric Kruskal–Wallis test or, in the case of pairwise comparison, the Mann–Whitney
U-test. Statistical significance was accepted at p < 0.05.
4. Conclusions
Our results indicate a delay in the establishment of the normal microbiota function in preterm
infants. Moreover, these early microbiota alterations are further affected by the use of perinatal
antibiotic, such as intrapartum prophylaxis, in these infants. This alteration in the early microbiota
establishment may have profound consequences for later health. Therefore, it would be advisable
to develop strategies aimed at limiting the impact of prematurity and antibiotics use upon the
establishing microbiota.
Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/
17/5/649/s1.
Acknowledgments: This work was founded by the EU Joint Programming Initiative–A Healthy Diet for a
Healthy Life (JPI HDHL, http://www.healthydietforhealthylife.eu/) and the Spanish Ministry of Economy
and Competitiveness (MINECO) (Project EarlyMicroHealth). Borja Sánchez was the recipient of a Ramón y
Cajal Postdoctoral contract (RYC-2012-10052) from MINECO. The Grant GRUPIN14-043 from “Plan Regional de
Investigación del Principado de Asturias” is also acknowledged.
Author Contributions: Gonzalo Solís, Nuria Fernández, Clara G. de los Reyes-Gavilán, Abelardo Margolles,
and Miguel Gueimonde designed the research; Silvia Arboleya, Borja Sánchez, Gonzalo Solís, Nuria Fernández,
Marta Suárez, and Miguel Gueimonde performed the research; Borja Sánchez, Ana M. Hernández-Barranco,
Christian Milani, and Marco Ventura contributed new reagents/analytic tools; Silvia Arboleya, Borja Sánchez,
Christian Milani, Marco Ventura, and Miguel Gueimonde analyzed data; and Silvia Arboleya, Gonzalo Solís,
Clara G. de los Reyes-Gavilán, Marco Ventura, Abelardo Margolles, and Miguel Gueimonde wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
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